void SoftCascadeLearner::classify(const nor_utils::Args& args)
{
SoftCascadeClassifier classifier(args, _verbose);
string testFileName = args.getValue<string>("test", 0);
string shypFileName = args.getValue<string>("test", 1);
int numIterations = args.getValue<int>("test", 2);
string outResFileName = "";
if ( args.getNumValues("test") > 3 )
args.getValue("test", 3, outResFileName);
classifier.run(testFileName, shypFileName, numIterations, outResFileName);
}
void FilterBoostLearner::classify(const nor_utils::Args& args)
{
FilterBoostClassifier classifier(args, _verbose);
// -test <dataFile> <shypFile>
string testFileName = args.getValue<string>("test", 0);
string shypFileName = args.getValue<string>("test", 1);
int numIterations = args.getValue<int>("test", 2);
string outResFileName;
if ( args.getNumValues("test") > 3 )
args.getValue("test", 3, outResFileName);
classifier.run(testFileName, shypFileName, numIterations, outResFileName);
}
void AdaBoostMHLearner::doPosteriors(const nor_utils::Args& args)
{
AdaBoostMHClassifier classifier(args, _verbose);
int numofargs = args.getNumValues( "posteriors" );
// -posteriors <dataFile> <shypFile> <outFile> <numIters>
string testFileName = args.getValue<string>("posteriors", 0);
string shypFileName = args.getValue<string>("posteriors", 1);
string outFileName = args.getValue<string>("posteriors", 2);
int numIterations = args.getValue<int>("posteriors", 3);
int period = 0;
if ( numofargs == 5 )
period = args.getValue<int>("posteriors", 4);
classifier.savePosteriors(testFileName, shypFileName, outFileName, numIterations, period);
}
void SoftCascadeLearner::getArgs(const nor_utils::Args& args)
{
if ( args.hasArgument("verbose") )
args.getValue("verbose", 0, _verbose);
///////////////////////////////////////////////////
// get the output strong hypothesis file name, if given
if ( args.hasArgument("shypname") )
args.getValue("shypname", 0, _shypFileName);
else
_shypFileName = string(SHYP_NAME);
_shypFileName = nor_utils::addAndCheckExtension(_shypFileName, SHYP_EXTENSION);
///////////////////////////////////////////////////
//TODO : create an abstract classe for cascade compliant base learners and accept only its offspring!
// get the name of the learner
_baseLearnerName = defaultLearner;
if ( args.hasArgument("learnertype") )
args.getValue("learnertype", 0, _baseLearnerName);
// cout << "! Only HaarSingleStumpeLearner is allowed.\n";
// -train <dataFile> <nInterations>
if ( args.hasArgument("train") )
{
args.getValue("train", 0, _trainFileName);
args.getValue("train", 1, _numIterations);
}
// -traintest <trainingDataFile> <testDataFile> <nInterations>
else if ( args.hasArgument("traintest") )
{
args.getValue("traintest", 0, _trainFileName);
args.getValue("traintest", 1, _testFileName);
args.getValue("traintest", 2, _numIterations);
}
// The file with the step-by-step information
if ( args.hasArgument("outputinfo") )
args.getValue("outputinfo", 0, _outputInfoFile);
else
_outputInfoFile = OUTPUT_NAME;
// --constant: check constant learner in each iteration
if ( args.hasArgument("constant") )
_withConstantLearner = true;
if ( args.hasArgument("positivelabel") )
{
args.getValue("positivelabel", 0, _positiveLabelName);
} else {
cout << "Error : The name of positive label must to given. \n Type --h softcascade to know the mandatory options." << endl;
exit(-1);
}
if (args.hasArgument("trainposteriors")) {
args.getValue("trainposteriors", 0, _trainPosteriorsFileName);
}
if (args.hasArgument("testposteriors")) {
args.getValue("testposteriors", 0, _testPosteriorsFileName);
}
if (args.hasArgument("detectionrate")) {
args.getValue("detectionrate", 0, _targetDetectionRate);
}
else {
cout << "Error : the target detection rate must be given. \n Type --h softcascade to know the mandatory options.";
exit(-1);
}
if (args.hasArgument("expalpha")) {
args.getValue("expalpha", 0, _alphaExponentialParameter);
}
else {
cout << "Error : the parameter used to initialize the rejection distribution vector must be given. \n Type --h softcascade to know the mandatory options.";
exit(-1);
}
if (args.hasArgument("calibrate")) {
args.getValue("calibrate", 0, _unCalibratedShypFileName);
if (args.getNumValues("calibrate") > 1) {
args.getValue("calibrate", 0, _inShypLimit);
}
}
else {
_fullRun = true;
_unCalibratedShypFileName = "shypToBeCalibrated.xml";
cout << "The strong hypothesis file will be seved into the file " << _unCalibratedShypFileName;
//cout << "Error : the shyp file of the uncalibrated trained classifier must be given ! \n";
//exit(-1);
}
if (args.hasArgument("bootstrap")) {
cout << "Warning ! The bootstrapping set and the training set must come from the same superset. \n";
args.getValue("bootstrap", 0, _bootstrapFileName);
args.getValue("bootstrap", 1, _bootstrapRate);
}
}